code: def get_regular(n_clusters):
      # spiral pattern
      centers = [
          [0, 0],
          [1, 0],
          [1, 1],
          [0, 1],
          [-1, 1],
          [-1, 0],
          [-1, -1],
          [0, -1],
          [1, -1],
          [2, -1],
      ][:n_clusters]
      assert len(centers) == n_clusters
      X, labels = make_blobs(
          n_samples=N_SAMPLES,
          centers=centers,
          cluster_std=0.25,
          random_state=SEED,
      )
      return normalize(X), labels
  
imports: import gradio
  import math
  from functools import partial
  import matplotlib.pyplot
  import numpy
  from sklearn.cluster import AgglomerativeClustering, Birch, DBSCAN, KMeans, MeanShift, OPTICS, SpectralClustering, estimate_bandwidth
  from sklearn.datasets import make_blobs, make_circles, make_moons
  from sklearn.mixture import GaussianMixture
  from sklearn.neighbors import kneighbors_graph
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 2 for calling a global function with example usage: get_regular(n_clusters) and returns: normalize,  labels